Bibliographic record
Abstract
Abstract There has been a revival of warfare and threats of interstate war in recent years as the number of countries engaged in armed conflict surged dramatically, reaching levels unprecedented since the end of the Cold War. This is happening at a time when the global burden of mental health illness is also on the rise. We examine the causal impact of early life exposure to warfare on long–term mental health, using novel data on the amount of bombs dropped in German cities by Allied Air Forces during World War II (WWII) and the German Socio-economic Panel. Our identification strategy leverages a generalized difference-indifferences design, exploiting the plausibly exogenous city-by-cohort variation in the bombing intensity experienced by the former West German cities during the war as a quasi- experiment. We find that cohorts who were five years old or younger during WWII have significantly poorer mental health outcomes later in life, when they are in their late 50s to 70s. Specifically, an increase of one standard deviation in the bombing intensity experienced during WWII is associated with about a 10 percent decline in an individual’s long–term standardized mental health score. This effect is equivalent to a 16.2 percent increase in the likelihood of being diagnosed with clinical depression. Our investigation suggests that factors such as the increased burden on the healthcare system, and economic losses during WWII exacerbate the adverse impact of bombing exposure on long–term mental health. Conversely, war relief funds transferred to municipalities following the war have a mitigating impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.204 | 0.056 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".